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Combining specificity determining and conserved residues improves functional site prediction

BACKGROUND: Predicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most like...

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Autores principales: Kalinina, Olga V, Gelfand, Mikhail S, Russell, Robert B
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2709924/
https://www.ncbi.nlm.nih.gov/pubmed/19508719
http://dx.doi.org/10.1186/1471-2105-10-174
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author Kalinina, Olga V
Gelfand, Mikhail S
Russell, Robert B
author_facet Kalinina, Olga V
Gelfand, Mikhail S
Russell, Robert B
author_sort Kalinina, Olga V
collection PubMed
description BACKGROUND: Predicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core). Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities. RESULTS: Here we present a novel method for functional site prediction based on identification of conserved positions, as well as those responsible for determining ligand specificity. We define Specificity-Determining Positions (SDPs), as those occupied by conserved residues within sub-groups of proteins in a family having a common specificity, but differ between groups, and are thus likely to account for specific recognition events. We benchmark the approach on enzyme families of known 3D structure with bound substrates, and find that in nearly all families residues predicted by SDPsite are in contact with the bound substrate, and that the addition of SDPs significantly improves functional site prediction accuracy. We apply SDPsite to various families of proteins containing known three-dimensional structures, but lacking clear functional annotations, and discusse several illustrative examples. CONCLUSION: The results suggest a better means to predict functional details for the thousands of protein structures determined prior to a clear understanding of molecular function.
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spelling pubmed-27099242009-07-14 Combining specificity determining and conserved residues improves functional site prediction Kalinina, Olga V Gelfand, Mikhail S Russell, Robert B BMC Bioinformatics Research Article BACKGROUND: Predicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core). Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities. RESULTS: Here we present a novel method for functional site prediction based on identification of conserved positions, as well as those responsible for determining ligand specificity. We define Specificity-Determining Positions (SDPs), as those occupied by conserved residues within sub-groups of proteins in a family having a common specificity, but differ between groups, and are thus likely to account for specific recognition events. We benchmark the approach on enzyme families of known 3D structure with bound substrates, and find that in nearly all families residues predicted by SDPsite are in contact with the bound substrate, and that the addition of SDPs significantly improves functional site prediction accuracy. We apply SDPsite to various families of proteins containing known three-dimensional structures, but lacking clear functional annotations, and discusse several illustrative examples. CONCLUSION: The results suggest a better means to predict functional details for the thousands of protein structures determined prior to a clear understanding of molecular function. BioMed Central 2009-06-09 /pmc/articles/PMC2709924/ /pubmed/19508719 http://dx.doi.org/10.1186/1471-2105-10-174 Text en Copyright © 2009 Kalinina et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Kalinina, Olga V
Gelfand, Mikhail S
Russell, Robert B
Combining specificity determining and conserved residues improves functional site prediction
title Combining specificity determining and conserved residues improves functional site prediction
title_full Combining specificity determining and conserved residues improves functional site prediction
title_fullStr Combining specificity determining and conserved residues improves functional site prediction
title_full_unstemmed Combining specificity determining and conserved residues improves functional site prediction
title_short Combining specificity determining and conserved residues improves functional site prediction
title_sort combining specificity determining and conserved residues improves functional site prediction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2709924/
https://www.ncbi.nlm.nih.gov/pubmed/19508719
http://dx.doi.org/10.1186/1471-2105-10-174
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